Statistical properties of natural images, which have been studied for the last several decades, include shift-invariance (stationarity), scale-invariance (especially in 2D projections of a 3D world viewed from various distances), high spatial auto-correlation (monochromatic objects or regions), spatial sparseness of edges (finite spatial extent of objects), and certain chromatic contrasts (preponderance of certain colors) [1]}, [2]}, [3]}, [4]}. Of these, only the shift-invariance has been widely exploited in neural architectures for image analysis in the form of convolutional filters [5]} and other architectural elements. There have been some exceptions that incorporate rotational-invariance for remote sensing images [6]}, and multi-resolution analysis for segmentation of histopathology images [7]}, [8]}, but these methods have not been tested on general computer vision benchmarks.
